mne_features.bivariate.compute_max_cross_corr¶

mne_features.bivariate.compute_max_cross_corr(sfreq, data, include_diag=False)¶

Maximum linear cross-correlation.

Parameters

sfreqfloat

Sampling rate of the data.

datandarray, shape (n_channels, n_times)

The signals.

include_diagbool (default: False)

If False, features corresponding to pairs of identical electrodes are not computed. In other words, features are not computed from pairs of electrodes of the form (ch[i], ch[i]).

Returns

outputndarray, shape (n_output,)

With n_output = n_channels * (n_channels + 1) / 2 if include_diag is True and n_output = n_channels * (n_channels - 1) / 2 if include_diag is False.

Notes

Alias of the feature function: max_cross_corr. See [1] and [2].

References

1

Mormann, F. et al. (2006). Seizure prediction: the long and winding road. Brain, 130(2), 314-333.

2

Mirowski, P. W. et al. (2008). Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG. Machine Learning for Signal Processing, 2008. IEEE Workshop on (pp. 244-249). IEEE.